Generating and using a predictive virtual personification

• US 10,210,425 B2
• Filed: 06/30/2017
• Issued: 02/19/2019
• Est. Priority Date: 07/18/2014
• Status: Active Grant

• Current Assignee: Acrovirt, LLC
• Original Assignee: Acrovirt, LLC
• Inventors: Caldwell, James Michael
• Primary Examiner(s): Demeter, Hilina K
• Application Number:
  US15/640,171
• Publication Number:
  US 20170316280A1
• Time in Patent Office: 599 Days
• Field of Search: None
• CPC Class Codes: A63F 2300/6607 for animating game characters, e.g. skeleton kinematics
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training

Abstract

A system for generating a predictive virtual personification includes a wearable data capture device, a data store, and a saliency recognition engine, wherein the wearable data acquisition device is configured to transmit one or more physio-emotional or neuro-cognitive data sets and a graphical representation of a donor subject to the saliency recognition engine, and the saliency recognition engine is configured to receive the one or more physio-emotional or neuro-cognitive data sets, the graphical representation, and one or more identified trigger stimulus events, locate a set of saliency regions of interest (SROI) within the graphical representation of the donor subject, generate a set of SROI specific saliency maps and store, in the data store, a set of correlated SROI specific saliency maps generated by correlating each SROI specific saliency map a corresponding trigger event.

Claims

1. A method for generating a predictive virtual personification comprises:

receiving a data set comprising a graphical representation of a donor subject;

receiving, from a wearable data capture device, a neuro-cognitive data set;

locating within the data set, with a saliency recognition engine, a set of saliency regions of interest (SROI) within said graphical representation of the donor subject;

identifying one or more trigger stimulus events, wherein each trigger stimulus event precedes or is contemporaneous with one or more SROI specific reactive responses and each SROI specific reactive response is observable within an SROI;

temporally correlating the SROI specific reactive response with a subset of the neuro-cognitive data set;

analyzing the subset of the neuro-cognitive or physio-emotional data set to identify a set of donor-specific physio-emotional or neuro-cognitive characteristics corresponding to a donor-specific physio-emotional or neuro-cognitive state at the time of the trigger stimulus event;

generating, for each SROI, a set of SROI specific saliency maps, wherein each SROI specific saliency map plots a change in of one or more SROls within a predetermined time-frame corresponding to each trigger stimulus event; and

storing, in a data store, a set of correlated SROI specific saliency maps generated by correlating each SROI specific saliency map to a corresponding trigger event and the donor-specific physio-emotional or neuro-cognitive state.

2. The method of claim 1, further comprising confirming the identification of the identified set of donor-specific physio-emotional or neuro-cognitive characteristics by receiving one or more physio-emotional characteristic tags corresponding to a known physio-emotional state of the donor subject.

3. The method of claim 2, wherein the receiving of one or more physio-emotional characteristic tags comprises receiving input from a user interface.

4. The method of claim 2, wherein the identifying a set of donor-specific physio-emotional or neuro-cognitive characteristics comprises using a deep learning algorithm to match one or more correlated SROI specific saliency maps with a plurality of historical SROI specific saliency maps, wherein each historical SROI specific saliency map corresponds to a set of physio-emotional characteristics.

5. The method of claim 4, wherein the matching the one or more correlated SROI specific saliency maps with a plurality of historical SROI specific saliency maps comprises applying, with a predictive virtual personification (PVP) correlation engine, a renormalization group transformation to each historical specific saliency map to generate a predictive saliency map space.

6. The method of claim 1, further comprising confirming the identification of a set of donor-specific neuro-cognitive characteristics from the neuro-cognitive data set by receiving one or more neuro-cognitive characteristic tags corresponding to a known neuro-cognitive state of the donor subject.

7. The method of claim 6, wherein the receiving of one or more neuro-cognitive characteristic tags comprises receiving input from a user interface.

8. The method of claim 7, wherein the identifying a set of donor-specific physio-emotional or neuro-cognitive characteristics comprises using a deep learning algorithm to match one or more correlated SROI specific saliency maps with a plurality of historical SROI specific saliency maps, wherein each historical SROI specific saliency map corresponds to a set of neuro-cognitive characteristics.

9. The method of claim 8, wherein the matching the one or more correlated SROI specific saliency maps with a plurality of historical SROI specific saliency maps comprises applying, with a predictive virtual personification (PVP) correlation engine, a renormalization group transformation to each historical specific saliency map to generate a predictive saliency map space.

10. The method of claim 1, wherein the set of donor-specific physio-emotional or neuro-cognitive characteristics comprises mood, level of rest, level of stress, or health.

11. The method of claim 1, further comprising generating, with a graphical rendering engine, an animated representation of the donor subject using the data set.

12. The method of claim 11, further comprising exposing the animated representation of the donor subject to a secondary stimulus event and rendering for each SROI, with a predictive virtual personification (PVP) rendering engine, a predicted reactive response.

13. The method of claim 12, wherein the rendering a predicted reactive response comprises identifying a secondary set of physio-emotional or neuro-cognitive characteristics corresponding to the animated representation of the donor subject;

identifying one or more trigger stimulus events corresponding to the secondary stimulus event;

receiving, from the data store, each set of correlated SROI specific saliency maps corresponding to each identified trigger stimulus event and to the identified set of physio-emotional or neuro-cognitive characteristics; and

generating, with the PVP rendering engine, a set of predictive SROI-specific saliency maps based on a probabilistic extrapolation as a function of the correlated SROI specific saliency maps, the identified physio-emotional or neuro-cognitive characteristics, and the identified trigger stimulus event.

14. The method of claim 13, wherein the generating a set of predictive SROI-specific saliency maps comprises collecting the correlated SROI specific saliency maps into a historical saliency map space and applying a deep learning algorithm to the historical saliency map space to generate a predictive saliency map space.

15. The method of claim 14, further comprising rendering, with the graphical rendering engine, a geospatial movement of the animated representation of the donor subject by applying the set of predictive SROI-specific saliency maps to each SROI within the animated representation of the donor subject.

16. The method of claim 13, wherein the generating a set of predictive SROI-specific saliency maps comprises collecting the correlated SROI specific saliency maps into a historical saliency map space and applying a renormalization group transformation to the historical saliency map space to generate a predictive saliency map space.

17. A system for generating a predictive virtual personification comprises:

a wearable data acquisition device, a data store, and a saliency recognition engine;

wherein the wearable data acquisition device is configured to transmit a physio-emotional or neuro-cognitive set to the saliency recognition engine; and

the saliency recognition engine comprises a non-transitory computer readable medium with a set of computer executable instructions stored thereon, the computer executable instructions configured to receive the physio-emotional or neuro-cognitive data set, a geospatial data set comprising a graphical representation of a donor subject, and one or more identified trigger stimulus events, wherein each identified trigger stimulus events precedes or is contemporaneous with one or more saliency regions of interest (SROI) specific reactive responses and each SROI specific reactive response is observable within an SROI;

locate a set of SROI within the graphical representation of the donor subject;

generate, for each SROI, a set of SROI specific saliency maps, wherein each SROI specific saliency map plots a change in geospatial orientation of one or more SROIs within a predetermined time-frame corresponding to each trigger stimulus event;

temporally correlate the SROI specific reactive response with a subset of the physio-emotional or neuro-cognitive data set;

analyze the subset of the physio-emotional or neuro-cognitive data set to identify a set of donor-specific physio-emotional or neuro-cognitive characteristics corresponding to a donor-specific physio-emotional state at the time of the trigger stimulus event; and

store, in the data store, a set of correlated SROI specific saliency maps generated by correlating each SROI specific saliency map a corresponding trigger event and the donor-specific physio-emotional or neuro-cognitive state.

18. The system of claim 17, wherein the wearable data acquisition device comprises an MEG, an EEG, a video camera, or a motion capture device (MOCAP).

19. The system of claim 17, wherein the saliency recognition engine is further configured to identify the set of donor-specific physio-emotional or neuro-cognitive characteristics corresponding to the donor-specific physio-emotional state and tag the set of correlated SROI specific saliency maps with the corresponding set of donor-specific physio-emotional or neuro-cognitive characteristics.

20. The system of claim 17, wherein the saliency recognition engine is further configured to apply a deep-learning algorithm to identify the set of donor-specific physio-emotional or neuro-cognitive characteristics corresponding to the donor-specific physio-emotional state and tag the set of correlated SROI specific saliency maps with the corresponding set of donor-specific physio-emotional or neuro-cognitive characteristics.

21. The system of claim 17, further comprising a graphical rendering engine configured to generate an animated representation of the donor subject based on the physio-emotional or neuro-cognitive data.

22. The system of claim 21, further comprising a predictive virtual personification (PVP) rendering engine configured to generate a predicted reactive response to a secondary stimulus event by applying a deep learning algorithm.

23. The system of claim 22, wherein the PVP rendering engine is further configured to:

identify a secondary set of physio-emotional or neuro-cognitive characteristics corresponding to the animated representation of the donor subject;

identify one or more trigger stimulus events corresponding to the secondary stimulus event;

receive, from the data store, each set of correlated SROI specific saliency maps that correspond to each identified trigger stimulus event and to the identified set of physio-emotional or neuro-cognitive characteristics; and

generate a set of predictive SROI-specific saliency maps based on a probabilistic extrapolation as a function of the correlated SROI specific saliency maps, the identified physio-emotional or neuro-cognitive characteristics, and the identified trigger stimulus event.

24. The system of claim 23, wherein the graphical rendering engine is further configured to render geospatial movement of the animated representation of the donor subject by applying the set of predictive SROI-specific saliency maps to each SROI within the animated representation of the donor subject.